Institutional Forecasting: The Performance of Thin Virtual Stock Markets
AbstractWe study the performance of Virtual Stock Markets (VSMs) in an institutional forecasting environment. We compare VSMs to the Combined Judgmental Forecast (CJF) and the Key Informant (KI) approach. We find that VSMs can be effectively applied in an environment with a small number of knowledgeable informants, i.e., in thin markets. Our results show that none of the three approaches differ in forecasting accuracy in a low knowledge-heterogeneity environment. However, where there is high knowledge-heterogeneity, the VSM approach outperforms the CJF approach, which in turn outperforms the KI approach. Hence, our results provide useful insight into when each of the three approaches might be most effectively applied.
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Bibliographic InfoPaper provided by Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam. in its series Research Paper with number ERS-2006-028-MKT.
Date of creation: 23 Jun 2006
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Forecasting; Electronic Markets; Information Markets; Virtual Stock Markets;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-09-11 (All new papers)
- NEP-FMK-2006-09-11 (Financial Markets)
- NEP-FOR-2006-09-11 (Forecasting)
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